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arXiv:2609.20799

Parametric Neural Quantum States for Nuclear Physics

Yukari Yamauchi, Ryan Curry, G. B. King, Scott Lawrence, Rahul Somasundaram, Ingo Tews

Published Sep 17, 2026• 0 citations
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Abstract

We introduce a new approach, based on neural quantum states (NQSs), to rapidly compute nuclear observables when couplings in nuclear Hamiltonians are varied. A single interaction-dependent NQS, trained across a range of couplings in the Hamiltonian, provides high-fidelity wavefunctions for that continuous range of interaction parameters. With access to the wavefunction for each set of couplings, any static observables can be computed efficiently without retraining the NQS. We apply this framework to two-body nuclear scattering and the deuteron ground state with local interactions derived from chiral effective field theory up to third order.

DOI
10.48550/arxiv.2609.20799

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We introduce a new approach, based on neural quantum states (NQSs), to rapidly compute nuclear observables when couplings in nuclear Hamiltonians are varied. A single interaction-dependent NQS, trained across a range of couplings in the Hamiltonian, provides high-fidelity wavefunctions for that continuous range of interaction parameters. With access to the wavefunction for each set of couplings, any static observables can be computed efficiently without retraining the NQS. We apply this framework to two-body nuclear scattering and the deuteron ground state with local interactions derived from chiral effective field theory up to third order.
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A long-standing goal of nuclear theory is to explain how the structure and dynamics of atomic nuclei and neutron-star matter emerge from the underlying interactions among protons and neutrons. Achieving this goal requires solving the nuclear quantum many-body problem with high accuracy across a wide range of length scales and density regimes. In this review, we discuss how artificial neural network representations of the nuclear many-body wave function have significantly extended the capabilities of continuum quantum Monte Carlo methods. In particular, neural network quantum states enable calculations of larger systems than were previously accessible and provide a flexible framework for capturing phenomena that challenge conventional approaches, including the emergence of nuclear clusters and superfluid phases in dense matter. We highlight recent applications to finite nuclei, infinite nuclear and neutron matter, and dynamical processes relevant to lepton-nucleus and nucleus-nucleus scattering. We also discuss conceptual and methodological connections with condensed matter physics, emphasizing developments in neural network quantum states that bridge strongly correlated systems across disciplines. Together, these developments demonstrate how neural-network methods open new avenues toward unified and accurate descriptions of nuclear structure, matter, and reactions.